The Wireless Localization Matching Problem

Nguyen, Cam Ly; Georgiou, Orestis; Yonezawa, Yoshiyuki; Doi, Yusuke

doi:10.1109/jiot.2017.2723013

Cited by 26 publications

(43 citation statements)

References 21 publications

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“…Therefore, they are inapplicable to RSSbased localization in which normal RSS measurements contain large error. Considering RSS measurements, the authors [6] use a graph partitioning method to detect and remove NLoS signals that travel through walls.…”

Section: A Noise Reduction Methodsmentioning

confidence: 99%

“…It, however, can also be combined with the above techniques to further reduce the effect of noise. For instance, outlier RSS measurements are first detected and removed using [6]. A smoothing method such as averaging is then applied to reduce the effect of shadowing.…”

Section: A Noise Reduction Methodsmentioning

confidence: 99%

“…The Cramer-Rao lower bound (CRLB) can give a lower bound on the covariance of localization error for a given environment is proportional to the ratio σ/η [5]. More generally, the authors have previously shown theoretically and experimentally that the accuracy of localization matching algorithms scales with the ratio η/σ [6]. Hence, the lower the σ/η ratio is, the higher localization accuracy a localization estimator can achieve.…”

Section: Background Intuitionmentioning

confidence: 99%

“…There are many cooperative localization algorithms such as multidimensional scaling (MDS), semidefinite programming (SDP) [15], stochastic optimization (e.g. simulated annealing (SA)) [3], and matching methods [6]. Among cooperative localization, SDP localization enhance accuracy and can provide deterministic solutions, while localization matching technique enhance accuracy when a set of node positions is provided.…”

Section: B Cooperative Localization Algorithmsmentioning

confidence: 99%

See 3 more Smart Citations

Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Nguyen

Georgiou

Suppakitpaisarn

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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Wireless localization methods are often subject to errors due to radio signal fluctuations that are used to estimate inter-device separation distances. We propose a novel method called MLRefine to counter these effects by refining RSS measurement data to obtain more accurate values that can enhance ranging and localization accuracies. MLRefine uses machine learning methods to model the relationship between accurate values and features extracted from in silico RSS values. MLRefine then applies the trained model to features extracted from real RSS measurement values to return a predicted set of refined RSS values. The refined RSS values are shown through computer simulations and real experiments to improve localization accuracy.

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Section: A Noise Reduction Methodsmentioning

confidence: 99%

Section: A Noise Reduction Methodsmentioning

confidence: 99%

Section: Background Intuitionmentioning

confidence: 99%

Section: B Cooperative Localization Algorithmsmentioning

confidence: 99%

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Improved Localization Accuracy Using Machine Learning: Predicting and Refining RSS Measurements

Nguyen

Georgiou

Suppakitpaisarn

2018

2018 IEEE Globecom Workshops (GC Wkshps)

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“…Recent indoor measurements at 2.4GHz [14] have confirmed the above model. where λ is a constant, and here we set λ = 0.561 because in this case the mean of X is zero [15]. A meaningful correspondence between X and our simplified Gaussian approximation X can be established (σ = 5.57).…”

Section: B Stability Of the Proposed Formulationmentioning

confidence: 99%

WiLAD: Wireless Localisation through Anomaly Detection

Nguyen

Khan

2017

GLOBECOM 2017 - 2017 IEEE Global Communications Conference

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Abstract-We propose a new approach towards RSS (Received Signal Strength) based wireless localisation for scenarios where, instead of absolute positioning of an object, only the information whether an object is inside or outside of a specific area is required. This is motivated through a number of applications including, but not limited to, a) security: detecting whether an object is removed from a secure location, b) wireless sensor networks: detecting sensor movements outside of a network area, and c) computational behaviour analytics: detecting customers leaving a retail store. The result of such detection systems can naturally be utilised in building a higher level contextual understanding of a system or user behaviours. We use a supervised learning method to overcome issues related to RSS based localisation systems including multipath fading, shadowing, and incorrect model parameters (as in unsupervised methods). Moreover, to reduce the cost of collecting training data, we employ a detection method called One-Class SVM (OC-SVM) which requires only one class of data (positive data, or target class data) for training. We derive a mathematical approximation of accuracy which utilises the characteristics of wireless signals as well as OC-SVM. Based on this we then propose a novel mathematical formula to find optimal placement of devices. This enables us to optimize the placement without performing any costly experiments or simulations. We validate our proposed mathematical framework based on simulated and real experiments.

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